Journal of Kufa for Mathematics and Computer

The effectiveness and dependability of a network can be examined using graph theory techniques, and the dependability of the network can be evaluated using its connection. Removing some vertices or edges can enhance a network\\'s diameter, which is a measure of its efficiency. In this paper, we calculate the maximum diameter ????????(????????) of the modified graph ???? with ???? (????≥????) vertices, which resulted from removing t edges from the complete graph ????????,(????≥????) was determined using the adjacency matrix ????????????(????????).

Handwritten digit recognition is one of the most active study areas in computer vision because to its numerous applications such as automatically identifying the digits in bank checks and car numbers. Handwritten Latin digits have been the subject of extensive research over the last three decades, whereas Persian handwritten digits have received far less attention. For this reason, we will concentrate on the problem of recognizing Persian (Farsi) handwritten numerals. The main challenge in the recognition of Persian handwritten digits is the presence of different patterns in Persian digit writing, which complicates the feature extraction process. An appropriate approach for automated feature extraction has been the focus of most earlier investigations since handcrafted feature extraction methods are complex and have unstable performance levels. This paper studies the use of a dataset of Persian handwritten digits generated by a Generative Adversarial Network (GAN) to develop a highly accurate Convolutional Neural Network (CNN) model for digit recognition. The proposed CNN architecture achieved a test accuracy of 99.7%, demonstrating its effectiveness. This study highlights the vi

These days we are constantly sharing and storing sensitive information online. From credit card numbers to personal identity details, our data is vulnerable to cyber-attacks and breaches. With the increase in cybercrime and hacking techniques, it is becoming increasingly important to protect our data through encryption.
This work aims to create a secure binary number of the system of encryption and to make cipher analysis impossible. We will combine Knapsack’s cipher with the equations of Bezier curves using the three-pass protocol.
The robustness of the proposed methods has been proved and the high security level by the experimental results, where the proposed methods have been more efficient and complex. The computational complexity of this method has been implemented in detail.

In this paper, we study basic geometric properties of analytic and univalent functions in the open unit disk. A new family ????????,????,???? ????,ℷ,????(????,????,????;ℵ) of analytic and univalent functions is defined by using a generalization of Tremblay fractional differential operator associated with the convolution product of a multiplier transformation and a Ruscheweyh derivative, especially. Coefficient-related studies and extreme points, distortion properties for the functions in class ????????,????,???? ????,ℷ,????(????,????,????;ℵ) are given and properties of starlikeness and the convexity of this class are also presented.

Crime and suspect prediction represent an immerging field where machine learning can be useful when applied efficiently. The available data can be used to train the proposed prediction model (building the model, classifier for example) and then this model can be tested and used to predict the crime type and suspect information such as sex, race and age category. The work in this paper presents a proposed approach for such prediction by using real world dataset available on the internet. The gathered data were preprocessed(deletion of the rows with missing and unknown attribute content, converting some attribute values into nominal or categorical data and concept hierarchy techniques) and reduced the number of attributes by choosing the most important features using different approaches and algorithms, then different Decision Tree types, Naïve Bayes Classifier and Association Rules prediction techniques were used to create the required models and to find out associations between different attributes of the given data. Testing phase shows high efficiency and effectiveness of the proposed approaches which in turn provide models that can be used as reliable predictors. An accuracy of more than 85% was achieved when using different classification techniques and the decision tree an accuracy of more than 85% due to its ability to classify the data based on important features, Naïve Bayes is less accurate than the decision tree in some cases, but usually achieves good accuracy ranging from 75% to 85% depending on the data. The decision tree is a powerful algorithm for extracting features that separate classes well, which leads to higher accuracy the Naïve Bayes uses probabilities to extract features but assumes independence of features, which may limit its accuracy in some cases. WEKA software, Microsoft Excel and XLSTAT mining software were used to analyze the given data.

In recent years, the medical industry has shown interest in reorganizing its operations to accommodate technological advancements and incorporating decision support systems into normal clinical procedures. Clinical Decision Support Systems (CDSS) combine observations and knowledge of health to encourage physicians to make health-related decisions in order to improve health care. The goal of this study is to examine the problems with Clinical Decision Support Systems (CDSS) and concentrate on their utility in improving clinical practice. This article contained a case study of CDSS implementation for high-risk pregnancy (HRPCDSS) and a description of the conditions for a successful CDSS implementation. A Clinical Decision Support System (HRPCDSS) was provided to assist doctors in estimating the probability of an illness, thereby enhancing their ability to provide therapeutic advice and eliminates the risk factors that community midwives'/lady health visitors encounter in providing standardized/effective healthcare services to mothers and children. Finally, using the C4.5 algorithm decision trees to identify these diseases and compare their effectiveness and correction rates.

Underwater Wireless Sensor Networks are necessary for many applications such as marine search and underwater monitoring like measuring water speeds, currents and temperatures. The challenge is, that the special underwater environment, which is, require specified routing and security protocols and the traditional protocols are suffer many problems. Therefore there is continuous motivation to search and develop to enhance its performance in viral applications. Vector Based Forwarding is an Underwater Wireless Sensor Networks protocol. This protocol is recognized by its dependency on default pipelines for routing, the packets are routed through these pipes. The disadvantages of this protocol are, the sensitivity to the radius of the pipe, which effects routing efficiency and in the case of spaces found (voids) the protocol may fail to find a communication path. This paper conducts enhancing on VBF by Link Quality Estimation to enhance packet delivery ratio, End to End delay and Energy Efficiency. The simulation was done and the final results which indicate to regarded enhancements in terms of Packets Delivery Ratio, reduced End to End Delay, and increased Energy Efficiency.